A novel framework for community modeling and characterization in directed temporal networks

We deal with the problem of modeling and characterizing the community structure ofcomplex systems. First, we propose a mathematical model for directed temporalnetworks based on the paradigm of activity driven networks. Many features ofreal-world systems are encapsulated in our model, such as hierarchical and overlappingcommunity structures, heterogeneous attitude of nodes in behaving as sources ordrains for connections, and the existence of a backbone of links that model dyadicrelationships between nodes. Second, we develop a method for parameteridentification of temporal networks based on the analysis of the integrated network ofconnections. Starting from any existing community detection algorithm, our methodenriches the obtained solution by providing an in-depth characterization of the verynature of the role of nodes and communities in generating the temporal link structure.The proposed modeling and characterization framework is validated on three syntheticbenchmarks and two real-world case studies.